A Three-stage Data Mining Model for Reject Inference

2012 ◽  
Author(s):  
Weimin Chen ◽  
Youjin Liu ◽  
Guocheng Xiang ◽  
Yongqing Liu ◽  
Kexi Wang
Keyword(s):  
Data Mining ◽  
Reject Inference ◽  
Mining Model

Research on Key Technology of Data Mining for Volleyball Game Based on Service System

2014 ◽  
Vol 543-547 ◽  
pp. 4698-4701
Author(s):  
Juan Wang
Keyword(s):  
Data Mining ◽  
High Precision ◽  
Service System ◽  
Processing System ◽  
Machining Process ◽  
Precision Machining ◽  
Intelligent Processing ◽  
Mining Model ◽  
Processing Cycle ◽  
Key Parameter

During the processing of aircraft and other high precision machinery workpieces, if using the traditional machining methods, it will consume a amount of machining costs, and the mechanical processing cycle is long. In this context, this paper designs a kind of robot intelligent processing system with high precision machinery. And it has realized the intelligent online control on the machining process by using the high precision machining intelligent online monitoring technology and the numerical simulation prediction technology. Finally, this system is introduced into the process of data mining for volleyball game, and designs the partial differential variational data mining model, which has realized the key parameter data mining of volleyball games service system, and has provided reliable parameters and technical support for the training of volleyball players.

scholarly journals Data Transformation Techniques for Academic Datasets

2019 ◽  
Vol 9 (1) ◽  
pp. 2214-2218
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Data Preprocessing ◽  
Data Normalization ◽  
Multi Layer Perceptron ◽  
Data Preparation ◽  
Transformation Techniques ◽  
Development Life Cycle ◽  
Heterogeneous Datasets ◽  
Mining Model

Data mining is a real-world procedure of discovering useful patterns from heterogeneous datasets. All most all industry uses data mining in their day to day activities. To build an effective mining model, a series of development steps are to be followed. It starts with discovering the business problem and ends with communicating the results. In this development life cycle, the most important step is data preparation or data preprocessing. Data preprocessing is converting raw data into data understandable by the machine. Data normalization is a phase in data preprocessing where the data values are scaled to 0 and 1. Right normalization of the datasets leads to improved mining results. In this paper, academic data of students is taken. The dataset is normalization using six normalization technique. Multi Layer Perceptron classifier is applied to normalized dataset and results are obtained. Results of this study reveal the best normalization technique which can be used for normalizing academic datasets. Finally, in a line, the goal of this work is to discover the best normalization technique which produces better mining result when applied to academic datasets.

Sign in / Sign up

Export Citation Format

Share Document